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Multiple Sequence Alignment A survey of the various programs available and application of MSA in addressing certain biological problems. Jeff Mower Kiran Annaiah. Sequence Comparison. Aligning two sequences is the cornerstone of Bioinformatics.

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Multiple Sequence AlignmentA survey of the various programs available and application of MSA in addressing certain biological problems

Jeff Mower

Kiran Annaiah

sequence comparison
Sequence Comparison
  • Aligning two sequences is the cornerstone of Bioinformatics.
    • Sequence alignment is the basic step upon which everything else built.
  • Sequence alignments are employed in
    • predicting de novo secondary structure of proteins,
    • The initial functional assignment
    • knowledge-based tertiary structure predictions,
    • Interpretation of data from genome sequencing projects
    • Inference of phylogenetic trees and resolution of ancestral relationships between species.
sequence comparisons
Sequence Comparisons
  • Homology Searches
    • Look for homologous sequences in databases using FASTA or BLAST program
  • Pattern Searches
    • Used for searching short sequence patterns
  • Multiple Sequence Alignment
    • For aligning and comparing 3 or more related sequences
  • Profile Analysis
    • A profile is created from a multiple sequence alignment
msa vs pairwise
MSA vs Pairwise
  • PSA

– based on seq. similarity we can identify unknown biological relationship

  • MSA
    • Similar to PSA
    • But also possible to identify conserved sub-patterns based on known biological relationship
  • High seq similarity – functional & structural similarity (PSA)
  • Sequences with functional and structural similarity can differ in sequences
    • PSA cannot detect this case
    • Example : Haemoglobin
msa vs pairwise5
MSA vs Pairwise
  • Structurally and functionally conserved molecules can differ in sequence – PSA cannot reveal conserved patterns
  • Comparing of 2 sequences with high similarity – patterns detection lost due to high similarity
  • MSA – useful in revealing critical patterns from multiple related sequences
multiple sequence alignment
Multiple Sequence Alignment
  • Homology
    • Homologous sequences, derive from common ancestor
    • Inferred by sequence similarity
    • MSA useful to demonstrate homology
      • Weak similarity – non-significant in pairwise comparison

could be highly significant if same residues are conserved

in other distantly related sequences

multiple sequence alignment7
Multiple Sequence Alignment
  • Global or Local Alignments
  • Substitution Matrices and weighting gaps
    • Best alignment is one that represents an evolutionary scenario
    • Mutational events in evolution considered in MSA
      • Substitutions
      • Insertions
      • Deletions
    • BLOSUM and PAM – based on evolutionary distances
    • Affine gap penalty model
      • p = a + bL
      • p = a + b blog(L)
  • Scoring MSA
    • Sum of Pairs score (SP) for columns
which msa method
Which MSA method…?
  • Global Methods
    • Optimal Algorithms (MSA, MWT, MUSEQAL)
  • Local methods
    • PIMA, DIALIGN, PRALINE, POA, MACAW, BlockMaker, Iteralign
  • Statistical (HMMT, SAGA, SAM, Match Box)
  • Parsimony(MALIGN, TreeAlign)
progressive algorithm
Progressive algorithm
  • Alignment is only an approximate solution (heuristic based)
  • Simplest and effective ways of MSA
  • Less time and less memory
  • Sequences are added one by one to the multiple alignment based on a pre-computed dendogram
  • Sequence addition is by PSA algorithm
  • Disadvantage
    • Once a sequence is aligned, cant be changed even if it conflicts with later sequence additions
  • Examples
    • PileUp, ClustalW, MultAlign, T-Coffee, etc
exact algorithms
Exact Algorithms
  • Useful in cases where sequences are extremely divergent
  • Simultaneously aligns all sequences
  • Disadvantage
    • Need to generalize Needleman-Wunsch algorithm
      • Only for a maximum of 3 sequences
      • Causes exponential increase in time and memory as number of sequences increase
  • Examples
    • MSA (up to 10 closely related sequences)
iterative algorithms
Iterative algorithms
  • Iterate over a existing sub-optimal solution, modifying it at each step, until a convergence point is met.
  • Examples
    • SAGA, AMPS, Praline, IterAlign
consistency based algorithms
Consistency-based Algorithms
  • Given a set of sequences, an optimal MSA is one that agrees most with all possible optimal pairwise alignments
    • Do not depend on specific subs. Matrix
    • Score associated with alignment of 2 residues depends on their indexes (position within protein sequence)
    • Most consistent are often the ones close to truth
  • Examples
    • T-Coffee, DiAlign
multiple alignment by profile hmm training
Multiple Alignment by profile HMM training

Given n sequences , consider the following cases:

  • If the profile HMM is known, the following procedure can be applied:
        • Align each sequence S(i) to the profile separately.
        • Accumulate the obtained alignments to a multiple alignment.
  • If the profile HMM is not known, one can use the following technique in order to obtain an HMM profile from the given sequences:
        • Choose a length L for the profile HMM and initialize the transition and emission probabilities.
        • Train the model using the Baum-Welch algorithm, on all the training sequences.
        • Obtain the multiple alignment from the resulting profile HMM, as in the previous case.

testing the methods
Testing the methods
  • BAliBASE benchmark
    • “Correct” Alignments
    • Core Blocks of Conserved Motifs
    • Typical “Hard Problem” Sets

Scores based on core blocks(V1)

Scores based on full-length alignment(V1)

  • Core blocks aligned well over long sequences – all programs
    • Due to different patterns of conservation
  • Alignment unreliable in the twilight zone
  • Iterative method did well under distinct alignment conditions, but not in the presence of an orphan sequence
  • Global algorithms – accurate and reliable for
    • equidistant sequences
    • divergent families of sequences and
    • alignment of orphan sequence with a family
  • Local algorithm – DiAlign
    • Best for N/C terminal extensions
    • Internal insertions
clustalw vs dialign vs t coffee vs poa
ClustalW vs DiAlign vs T-Coffee vs POA
  • ClustalW – global progressive method
    • Guide tree created
    • Successive pairwise alignment
  • Poa – progressive using partially ordered graphs
    • No tree to guide alignment of sequences
    • 2 most similar seqs are aligned and others are added to this one profile in a stepwise fashion
  • DiAlign – local algorithm
    • Aligns whole segments
    • PSA performed and ungapped alignments used
  • T-Coffee – progressive global & local
    • Performs PSA – twice, once global (ClustalW) and local (LAlign)
    • Results combined into primary library, then extension step
    • Progressive alignment using info from library
  • Poor performance by all programs with increase in evolutionary distance
  • Increase in seq length – better alignment
  • T-Coffee – best for low – moderate evolutionary distances
  • DiAlign good for high evolutionary distances
what can we do with msas
What Can We Do With MSAs?
  • Motif / pattern identification
  • Structural modeling
  • Phylogenetic analysis
  • Molecular evolutionary analyses
  • Identification of conserved genomic regions across species
phylogenetic analysis
Phylogenetic Analysis
  • Visual representation of a MSA as a tree of relationships
  • Many methods:
    • Distance: builds tree by clustering sequences according to their similarity
    • Parsimony: finds tree that minimizes the number of changes required
    • Maximum Likelihood: finds tree that maximizes likelihood given parameters
    • Bayesian: Markov chain Monte Carlo simulation to calculate posterior probabilities of trees
determining relationships
Determining Relationships



(Baldauf 2003)

rooted vs unrooted trees
Rooted vs Unrooted Trees



(Baldauf 2003)


Many taxa (567),

Few genes (3)

(Soltis et al. 1999)


Few taxa (13),

Many genes (61)

(Goremykin et al. 2003)


Many taxa,

Many genes

Coming Soon…


Human, Gorilla, & Chimp

Glycophorin gene family

(Wang et al. 2003)


(Baldauf 2003)

molecular evolution background
Molecular Evolution Background
  • Types of changes in protein-coding DNA
    • Silent (synonymous)
    • Replacement (nonsynonymous)
    • Based on degeneracy of the genetic code
  • K – frequency of change between two sequences
    • Ka: # of replacement changes per replacement site
      • Driven by natural selection
      • Reflect level of protein conservation
    • Ks: # of silent changes per silent site
      • Neutral, not affected by selective forces
      • Used to estimate the neutral mutation rate
estimation of substitution rates
Estimation of Substitution Rates
  • Absolute Rate
    • R = ½ K / T, where T = time since last common ancestor
    • Rates are directly comparable
  • Relative Rate
    • Two species of interest and one outgroup
    • Compare K from species 1 and outgroup against K from species 2 and outgroup
relative rate
Relative Rate


K Rat-Kan =

K Hum-Kan =




K Rat-Kan - K Hum-Kan =



evaluation of selective pressures the k a k s ratio
Evaluation of Selective Pressures, The Ka / Ks Ratio
  • Ka / Ks > 1 indicates positive selection
  • Ka / Ks ≈ 1 indicates no selection
  • Ka / Ks < 1 indicates purifying selection

280 homologs,


(Wang et al. 2003)

conserved genomic regions
Conserved Genomic Regions
  • Need complete genomes or homologous genomic regions
  • Identify exons from distantly related species
  • Identify regulatory elements from more closely related species
  • Biological Sequence Analysis – R. Durbin, S. Eddy, A. Krogh & G. Mitchison
  • Bioinformatics: Sequence, structure and databanks – D. Higgins & W. Taylor
  • Recent progress in multiple sequence alignment: a survey.

  • Quality assessment of multiple alignment programs.
  • Multiple sequence alignment with the Clustal series of programs.
  • MAVID multiple alignment server
  • Multiple alignment of sequences and structures.
  • Fast algorithms for large-scale genome alignment and comparison.